The SHOGUN Machine Learning Toolbox

Abstract

We have developed a machine learning toolbox, called SHOGUN, which is
designed for unified large-scale learning for a broad range of feature
types and learning settings. It offers a considerable number of machine
learning models such as support vector machines, hidden Markov models, multiple kernel learning, linear
discriminant analysis, and
more. Most of the specific algorithms are able to deal with
several different data classes. We
have used this toolbox in several applications from computational
biology, some of them coming with no less than 50 million training
examples and others with 7 billion test examples. With more than a
thousand installations worldwide, SHOGUN is already widely adopted in
the machine learning community and beyond.
SHOGUN is implemented in C++ and interfaces to MATLABTM, R, Octave, Python, and
has a stand-alone command line interface. The source code is freely available under the GNU General Public License, Version 3 at http://www.shogun-toolbox.org.